Recursive Least-Squares algorithms for sparse system modeling
Hamed Yazdanpanah, Paulo S. R. Diniz
Abstract
In this paper, we propose some sparsity aware algorithms, namely the Recursive least-Squares for sparse systems (S-RLS) and l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> -norm Recursive least-Squares (l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> -RLS), in order to exploit the sparsity of an unknown system. The first algorithm, applies a discard function on the weight vector to disregard the coefficients close to zero during the update process. The second algorithm, employs the sparsity-promoting scheme via some non-convex approximations to the l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> -norm. In addition, we consider the respective versions of these algorithms in data-selective versions in order to reduce the update rate. Simulation results show similar performance when comparing the proposed algorithms with standard Recursive Least-Squares (RLS) algorithm while the proposed algorithms require lower computational complexity.
BibTeX
@inproceedings{icassp2017_recursiveleastsq,
title = {Recursive Least-Squares algorithms for sparse system modeling},
author = {Hamed Yazdanpanah and Paulo S. R. Diniz},
booktitle = {ICASSP 2017},
year = {2017}
}